best_predictors_lr_part1.py

feature_to_coef = {
    word: coef for word, coef in zip(
        cv.get_feature_names(), final_model.coef_[0]
    )
}
for best_positive in sorted(
    feature_to_coef.items(), 
    key=lambda x: x[1], 
    reverse=True)[:5]:
    print (best_positive)
    
#     ('excellent', 0.9288812418118644)
#     ('perfect', 0.7934641227980576)
#     ('great', 0.675040909917553)
#     ('amazing', 0.6160398142631545)
#     ('superb', 0.6063967799425831)
    
for best_negative in sorted(
    feature_to_coef.items(), 
    key=lambda x: x[1])[:5]:
    print (best_negative)
    
#     ('worst', -1.367978497228895)
#     ('waste', -1.1684451288279047)
#     ('awful', -1.0277001734353677)
#     ('poorly', -0.8748317895742782)
#     ('boring', -0.8587249740682945)